# app.py
import streamlit as st
from dotenv import load_dotenv
import uuid
from utils import *

if 'unique_id' not in st.session_state:
    st.session_state['unique_id'] = ''


def main():
    load_dotenv()

    st.set_page_config(page_title="Resume Screening Assistance")
    st.title("HR - Resume Screening Assistance...💁 ")
    st.subheader("I can help you in resume screening process")

    job_description = st.text_area("Please paste the 'JOB DESCRIPTION' here...", key="1")
    document_count = st.text_input("No.of 'RESUMES' to return", key="2")
    # Upload the Resumes (pdf files)
    pdf = st.file_uploader(
        "Upload resumes here, only PDF files allowed", type=["pdf"], accept_multiple_files=True
    )

    submit = st.button("Help me with the analysis")

    if submit:
        with st.spinner('Wait for it...'):
            # st.write("our process")
            # Creating a unique ID, so that we can use to query and get only the user uploaded documents from PINECONE vector store
            st.session_state['unique_id'] = uuid.uuid4().hex

            # Create a documents list out of all the user uploaded pdf files
            docs = create_docs(pdf, st.session_state['unique_id'])

            # Displaying the count of resumes that have been uploaded
            st.write(len(docs))

            # Create embeddings instance
            embeddings = create_embeddings_load_data()

            # Push data to PINECONE
            push_to_pinecone(docs, embeddings)

            # Fecth relavant documents from PINECONE
            relavant_docs = similar_docs(
                job_description, document_count, embeddings, st.session_state['unique_id']
            )

            # Introducing a line separator -----
            st.write(":heavy_minus_sign:" * 30)

            # For each item in relavant docs - we are displaying some info of it on the UI
            for item in range(len(relavant_docs)):

                st.subheader("👉 " + str(item + 1))

                # Displaying Filepath
                st.write("**File** : " + relavant_docs[item][0].metadata['name'])

                # Introducing Expander feature
                with st.expander('Show me 👀'):
                    st.info("**Match Score** : " + str(relavant_docs[item][1]))
                    # st.write("***"+relavant_docs[item][0].page_content)

                    # Gets the summary of the current item using 'get_summary' function that we have created which uses LLM & Langchain chain
                    summary = get_summary(relavant_docs[item][0])
                    st.write("**Summary** : " + summary)

        st.success("Hope I was able to save your time❤️")


# Invoking main function
if __name__ == '__main__':
    main()
